Requirement Risk Level Forecast Using Bayesian Networks Classifiers
Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have d...
| Main Authors: | , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
Wordl Scientific
2016
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| Online Access: | http://dx.doi.org/10.1142/S0218194011005219 http://hdl.handle.net/10835/4465 |
| _version_ | 1789406601761783808 |
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| author | Del Águila Cano, Isabel María Sagrado Martínez, José del |
| author_facet | Del Águila Cano, Isabel María Sagrado Martínez, José del |
| author_sort | Del Águila Cano, Isabel María |
| collection | DSpace |
| description | Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-4465 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2016 |
| publisher | Wordl Scientific |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-44652023-04-12T19:26:04Z Requirement Risk Level Forecast Using Bayesian Networks Classifiers Del Águila Cano, Isabel María Sagrado Martínez, José del Requirement engineering is a key issue in the development of a software project. Like any other development activity it is not without risks. This work is about the empirical study of risks of requirements by applying machine learning techniques, specifically Bayesian networks classifiers. We have defined several models to predict the risk level for a given requirement using three dataset that collect metrics taken from the requirement specifications of different projects. The classification accuracy of the Bayesian models obtained is evaluated and compared using several classification performance measures. The results of the experiments show that the Bayesians networks allow obtaining valid predictors. Specifically, a tree augmented network structure shows a competitive experimental performance in all datasets. Besides, the relations established between the variables collected to determine the level of risk in a requirement, match with those set by requirement engineers. We show that Bayesian networks are valid tools for the automation of risks assessment in requirement engineering. 2016-11-02T07:41:06Z 2016-11-02T07:41:06Z 2011-03 info:eu-repo/semantics/article http://dx.doi.org/10.1142/S0218194011005219 1793-6403 http://hdl.handle.net/10835/4465 en http://www.worldscientific.com/doi/abs/10.1142/S0218194011005219 info:eu-repo/semantics/openAccess Wordl Scientific |
| spellingShingle | Del Águila Cano, Isabel María Sagrado Martínez, José del Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title | Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title_full | Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title_fullStr | Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title_full_unstemmed | Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title_short | Requirement Risk Level Forecast Using Bayesian Networks Classifiers |
| title_sort | requirement risk level forecast using bayesian networks classifiers |
| url | http://dx.doi.org/10.1142/S0218194011005219 http://hdl.handle.net/10835/4465 |
| work_keys_str_mv | AT delaguilacanoisabelmaria requirementrisklevelforecastusingbayesiannetworksclassifiers AT sagradomartinezjosedel requirementrisklevelforecastusingbayesiannetworksclassifiers |